Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements

Agrafiotis, D. K. et al. (2021) Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements. PLoS ONE, 16(1), e0245874. (doi: 10.1371/journal.pone.0245874) (PMID:33513170) (PMCID:PMC7845999)

Abstract

Objective: One of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of human movements collected longitudinally in patients after stroke would bear a significant relationship to the ordinal clinical scales and potentially lead to the development of more sensitive motor biomarkers that could improve the efficiency and cost of clinical trials. Materials and methods: We used clinical scales and a robotic assay to measure arm movement in 208 patients 7, 14, 21, 30 and 90 days after acute ischemic stroke at two separate clinical sites. The robots are low impedance and low friction interactive devices that precisely measure speed, position and force, so that even a hemiparetic patient can generate a complete measurement profile. These profiles were used to develop predictive models of the clinical assessments employing a combination of artificial ant colonies and neural network ensembles. Results: The resulting models replicated commonly used clinical scales to a cross-validated R2 of 0.73, 0.75, 0.63 and 0.60 for the Fugl-Meyer, Motor Power, NIH stroke and modified Rankin scales, respectively. Moreover, when suitably scaled and combined, the robotic measures demonstrated a significant increase in effect size from day 7 to 90 over historical data (1.47 versus 0.67). Discussion and conclusion: These results suggest that it is possible to derive surrogate biomarkers that can significantly reduce the sample size required to power future stroke clinical trials.

• Item Type: Articles
• Additional Information: Funding: This study was funded by a grant from Wyeth to HIK (Grant No. 6917860). Support for this study in the form of salaries was provided by the following: Janssen Research & Development (DA, EY, AD and MK), GSL Statistical Consulting (GL), Bioconstat Bvba (GB), and Biogen-Idec (JC).
• Keywords: Research Article, Engineering and technology, Medicine and health sciences, Computer and information sciences, Biology and life sciences, Physical sciences
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lees, Professor Kennedy and Hajjar, Dr Karim

Creator Roles

Lees, K.: Conceptualization, Supervision
Hajjar, K.: Investigation

• Authors: Agrafiotis, D. K., Yang, E., Littman, G. S., Byttebier, G., Dipietro, L., DiBernardo, A., Chavez, J. C., Rykman, A., McArthur, K., Hajjar, K., Lees, K., Volpe, B. T., Krams, M., and Krebs, H. I.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
• Journal Name: PLoS ONE
• Publisher: Public Library of Science
• ISSN: 1932-6203
• ISSN (Online): 1932-6203
• Copyright Holders: Copyright © 2021 Agrafiotis et al
• First Published: First Published PLoS ONE 16(1): e0245874
• Publisher Policy: Reproduced under a Creative Commons licence